Integrate “Forensics Copilot” Chat Panel with RAG into Autopsy #7986
Description
Type: Feature Discussion
Component: UI / Plugins / Search / Automation
Goal: Explore feasibility of adding an AI-assisted Copilot panel to Autopsy, powered by Retrieval-Augmented Generation (RAG).
Summary
I propose exploring the addition of an optional AI Copilot sidebar to Autopsy that assists investigators during case analysis.
The idea is to provide a guided, natural-language interface that references case data, helps draft reports, answers questions about artifacts, and automates certain tasks — all through a RAG layer that respects Autopsy’s forensic constraints.
From my master’s thesis on leveraging LLMs in Digital Forensics, I found satisfactory results in aiding investigations. With recent improvements in LLM models, this feature is now feasible and implementable.
Important: This is not intended to replace investigator judgment or make decisions. It is a Copilot, assisting investigators to access and process data more efficiently so they can generate their own insights.
Motivation
Digital forensic investigations often involve:
- Searching across large, heterogeneous datasets
- Repetitive analysis tasks
- Combining logs, files, and metadata manually
- Writing detailed case reports
A lightweight, optional Copilot panel could streamline these steps while keeping investigators fully in control. The goal is assistance, not automated decision-making.
High-Level Concept
A right-side “Copilot” chat panel inside Autopsy could:
-
Answer questions about the case
Example:“List all files modified on the suspect device between 02:00–03:00.”
-
Explain artifacts or modules
“What does the Web Cache artifact represent?”
-
Generate summaries
“Summarize all USB activity on this image.”
-
Draft report sections
Still requiring human review. -
Guide new investigators
“How do I run an EXIF analysis on all images?”
Underlying mechanism:
-
RAG pipeline that indexes:
- Case notes
- Extracted artifacts
- Logs
- File metadata
- Autopsy documentation
-
Local inference / offline mode preferred for chain-of-custody and privacy.
Proposed Architecture (Initial Thoughts)
- Plugin-based Copilot module
- Document/metadata indexer using existing Autopsy ingest formats
- Vector store: local, file-based (e.g., SQLite-FAISS, Chroma)
- LLM: user-provided, offline/local accepted (e.g., Llama models)
- UI: new pane similar to the Timeline or Keyword search windows
- Strict control:
- No automatic modification of case data
- Investigator must approve any actions
- Copilot only assists, does not generate insights or make decisions
Key Requirement: Transparency & Explainability
The biggest requirement for this use case is transparency and explainability (XAI). Any AI output must be traceable and interpretable:
- Copilot responses should reference the data sources (artifact files, metadata, logs)
- No “black-box” conclusions; investigators must always see why a suggestion was made
- Ensures trust and preserves forensic rigor
Considerations & Open Questions
Technical
- What internal APIs or extension points allow safe access to extracted artifacts?
- How to handle large case sizes efficiently in RAG indexing?
- How should Copilot interact with ingest modules and keyword searches?
Privacy & Compliance
- Should the module require full offline operation?
- What guarantees are needed for chain-of-custody preservation?
- How do we ensure explainability of AI output?
Philosophical / UX
- Copilot stays strictly as an information assistant, not a pilot.
- How to prevent over-reliance on AI interpretations while maximizing productivity?
Request for Feedback
Would the maintainers be open to:
- Exploring the idea further?
- Identifying the recommended extension points?
- Discussing acceptable boundaries for AI-assisted features within Autopsy?
If the concept seems viable, I can draft a more detailed design proposal or proof-of-concept plugin outline.